{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Water Distribution (WADI)\n",
    "\n",
    "- Source and description: https://itrust.sutd.edu.sg/itrust-labs_datasets/dataset_info/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from typing import List\n",
    "from pathlib import Path\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from config import data_raw_folder, data_processed_folder\n",
    "\n",
    "from timeeval import Datasets, DatasetManager\n",
    "from timeeval.datasets import DatasetAnalyzer, DatasetRecord"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (20, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking for source datasets in /home/projects/akita/data/benchmark-data/data-raw/WADI/WADI.A2_19 Nov 2019 and\n",
      "saving processed datasets in /home/projects/akita/data/benchmark-data/data-processed\n"
     ]
    }
   ],
   "source": [
    "dataset_collection_name = \"WADI\"\n",
    "source_folder = Path(data_raw_folder) / \"WADI\" / \"WADI.A2_19 Nov 2019\"\n",
    "target_folder = Path(data_processed_folder)\n",
    "\n",
    "print(f\"Looking for source datasets in {Path(source_folder).absolute()} and\\nsaving processed datasets in {Path(target_folder).absolute()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Created directories /home/projects/akita/data/benchmark-data/data-processed/multivariate/WADI\n"
     ]
    }
   ],
   "source": [
    "# shared by all datasets\n",
    "dataset_type = \"real\"\n",
    "input_type = \"multivariate\"\n",
    "datetime_index = True\n",
    "split_at = None\n",
    "train_is_normal = True\n",
    "train_type = \"semi-supervised\"\n",
    "\n",
    "# create target directory\n",
    "dataset_subfolder = Path(input_type) / dataset_collection_name\n",
    "target_subfolder = target_folder / dataset_subfolder\n",
    "target_subfolder.mkdir(parents=True, exist_ok=True)\n",
    "print(f\"Created directories {target_subfolder}\")\n",
    "\n",
    "dm = DatasetManager(target_folder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepare_dataset(df: pd.DataFrame, is_train: bool = False) -> pd.DataFrame:\n",
    "    # fix index\n",
    "    df.index = df.index.astype(np.int_) - 1\n",
    "    df.index.name = df.index.name.strip()\n",
    "\n",
    "    # fix column names\n",
    "    df.columns = [c.strip() for c in df.columns]\n",
    "\n",
    "    # fix timestamps\n",
    "    s_hour = (18 + (df.index // 3600)) % 24\n",
    "    s_timestamp = df[\"Date\"] + s_hour.map(\" {:02.0f}:\".format) + df[\"Time\"]\n",
    "    s_timestamp = pd.to_datetime(s_timestamp, infer_datetime_format=True)\n",
    "    df = df.drop(columns=[\"Date\", \"Time\"])\n",
    "    df.insert(0, \"timestamp\", s_timestamp)\n",
    "\n",
    "    # align anomaly label\n",
    "    df[\"is_anomaly\"] = 0\n",
    "    if not is_train:\n",
    "        s_label = df[\"Attack LABLE (1:No Attack, -1:Attack)\"]\n",
    "        df = df.drop(columns=[\"Attack LABLE (1:No Attack, -1:Attack)\"])\n",
    "        df.loc[s_label == -1, \"is_anomaly\"] = 1\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing dataset\n",
      "  written test dataset\n",
      "  loaded train datasets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[('WADI', 'WADI.A2') (test)] KPSS trend stationarity test for 1_LS_001_AL encountered an error: cannot convert float NaN to integer\n",
      "[('WADI', 'WADI.A2') (test)] KPSS trend stationarity test for 1_LS_002_AL encountered an error: cannot convert float NaN to integer\n",
      "[('WADI', 'WADI.A2') (test)] KPSS trend stationarity test for 1_P_002_STATUS encountered an error: cannot convert float NaN to integer\n",
      "[('WADI', 'WADI.A2') (test)] KPSS trend stationarity test for 1_P_004_STATUS encountered an error: cannot convert float NaN to integer\n"
     ]
    }
   ],
   "source": [
    "print(\"Processing dataset\")\n",
    "base_dataset_name = \"WADI.A2\"\n",
    "test_filename = f\"{base_dataset_name}.test.csv\"\n",
    "test_path = dataset_subfolder / test_filename\n",
    "target_test_filepath = target_subfolder / test_filename\n",
    "\n",
    "# prepare test dataset\n",
    "df_test = pd.read_csv(\n",
    "    source_folder / \"WADI_attackdataLABLE.csv\",\n",
    "    skiprows=1,\n",
    "    skip_blank_lines=True,\n",
    "    index_col=0\n",
    ")\n",
    "df_test = df_test.iloc[:-2, :]\n",
    "df_test = prepare_dataset(df_test, is_train=False)\n",
    "df_test.to_csv(target_test_filepath, index=False)\n",
    "print(\"  written test dataset\")\n",
    "\n",
    "# prepare train datasets\n",
    "df_train = pd.read_csv(\n",
    "    source_folder / \"WADI_14days_new.csv\",\n",
    "    skip_blank_lines=True,\n",
    "    index_col=0\n",
    ")\n",
    "df_train = prepare_dataset(df_train, is_train=True)\n",
    "# split to remove jump\n",
    "s = df_train[\"timestamp\"].diff()\n",
    "cut_index = s[s > pd.Timedelta(1, unit=\"s\")].index[0]\n",
    "dfs_train = []\n",
    "dfs_train.append(df_train.loc[:cut_index-1])\n",
    "dfs_train.append(df_train.loc[cut_index:])\n",
    "print(\"  loaded train datasets\")\n",
    "\n",
    "# analyze test dataset\n",
    "da = DatasetAnalyzer((dataset_collection_name, base_dataset_name), is_train=False, df=df_test)\n",
    "print(\"  analyzed test dataset\")\n",
    "\n",
    "for i in range(2):\n",
    "    dataset_name = f\"{base_dataset_name}-{i+1}\"\n",
    "    train_filename = f\"{dataset_name}.train.csv\"\n",
    "    train_path = dataset_subfolder / train_filename\n",
    "    target_train_filepath = target_subfolder / train_filename\n",
    "    target_meta_filepath = target_test_filepath.parent / f\"{dataset_name}.{Datasets.METADATA_FILENAME_SUFFIX}\"\n",
    "    \n",
    "    # overwrite dataset id\n",
    "    da.dataset_id = (dataset_collection_name, dataset_name)\n",
    "    meta = da.metadata\n",
    "    da.save_to_json(target_meta_filepath, overwrite=True)\n",
    "    print(\"  written test dataset metadata\")\n",
    "    \n",
    "    df_train = dfs_train[i]\n",
    "    df_train.to_csv(target_train_filepath, index=False)\n",
    "    print(f\"  written training dataset {i}\")\n",
    "\n",
    "    DatasetAnalyzer((dataset_collection_name, dataset_name), is_train=True, df=df_train)\\\n",
    "        .save_to_json(target_meta_filepath, overwrite=False)\n",
    "    print(f\"  analyzed training dataset {i}\")\n",
    "    print(f\"  written training dataset {i} metadata\")\n",
    "\n",
    "    dm.add_dataset(DatasetRecord(\n",
    "          collection_name=dataset_collection_name,\n",
    "          dataset_name=dataset_name,\n",
    "          train_path=train_path,\n",
    "          test_path=test_path,\n",
    "          dataset_type=dataset_type,\n",
    "          datetime_index=datetime_index,\n",
    "          split_at=split_at,\n",
    "          train_type=train_type,\n",
    "          train_is_normal=train_is_normal,\n",
    "          input_type=input_type,\n",
    "          length=meta.length,\n",
    "          dimensions=meta.dimensions,\n",
    "          contamination=meta.contamination,\n",
    "          num_anomalies=meta.num_anomalies,\n",
    "          min_anomaly_length=meta.anomaly_length.min,\n",
    "          median_anomaly_length=meta.anomaly_length.median,\n",
    "          max_anomaly_length=meta.anomaly_length.max,\n",
    "          mean=meta.mean,\n",
    "          stddev=meta.stddev,\n",
    "          trend=meta.trend,\n",
    "          stationarity=meta.get_stationarity_name(),\n",
    "          period_size=np.nan\n",
    "    ))\n",
    "    print(f\"... processed dataset with training file {i+1}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "dm.save()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>train_path</th>\n",
       "      <th>test_path</th>\n",
       "      <th>dataset_type</th>\n",
       "      <th>datetime_index</th>\n",
       "      <th>split_at</th>\n",
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       "      <th>train_is_normal</th>\n",
       "      <th>input_type</th>\n",
       "      <th>length</th>\n",
       "      <th>dimensions</th>\n",
       "      <th>contamination</th>\n",
       "      <th>num_anomalies</th>\n",
       "      <th>min_anomaly_length</th>\n",
       "      <th>median_anomaly_length</th>\n",
       "      <th>max_anomaly_length</th>\n",
       "      <th>mean</th>\n",
       "      <th>stddev</th>\n",
       "      <th>trend</th>\n",
       "      <th>stationarity</th>\n",
       "      <th>period_size</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>collection_name</th>\n",
       "      <th>dataset_name</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">WADI</th>\n",
       "      <th>WADI.A2-1</th>\n",
       "      <td>multivariate/WADI/WADI.A2-1.train.csv</td>\n",
       "      <td>multivariate/WADI/WADI.A2.test.csv</td>\n",
       "      <td>real</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>semi-supervised</td>\n",
       "      <td>True</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>172801</td>\n",
       "      <td>127</td>\n",
       "      <td>0.057737</td>\n",
       "      <td>14</td>\n",
       "      <td>88</td>\n",
       "      <td>651</td>\n",
       "      <td>1741</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>kubic trend</td>\n",
       "      <td>not_stationary</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WADI.A2-2</th>\n",
       "      <td>multivariate/WADI/WADI.A2-2.train.csv</td>\n",
       "      <td>multivariate/WADI/WADI.A2.test.csv</td>\n",
       "      <td>real</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>semi-supervised</td>\n",
       "      <td>True</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>172801</td>\n",
       "      <td>127</td>\n",
       "      <td>0.057737</td>\n",
       "      <td>14</td>\n",
       "      <td>88</td>\n",
       "      <td>651</td>\n",
       "      <td>1741</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>kubic trend</td>\n",
       "      <td>not_stationary</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                         train_path  \\\n",
       "collection_name dataset_name                                          \n",
       "WADI            WADI.A2-1     multivariate/WADI/WADI.A2-1.train.csv   \n",
       "                WADI.A2-2     multivariate/WADI/WADI.A2-2.train.csv   \n",
       "\n",
       "                                                       test_path dataset_type  \\\n",
       "collection_name dataset_name                                                    \n",
       "WADI            WADI.A2-1     multivariate/WADI/WADI.A2.test.csv         real   \n",
       "                WADI.A2-2     multivariate/WADI/WADI.A2.test.csv         real   \n",
       "\n",
       "                              datetime_index  split_at       train_type  \\\n",
       "collection_name dataset_name                                              \n",
       "WADI            WADI.A2-1               True       NaN  semi-supervised   \n",
       "                WADI.A2-2               True       NaN  semi-supervised   \n",
       "\n",
       "                              train_is_normal    input_type  length  \\\n",
       "collection_name dataset_name                                          \n",
       "WADI            WADI.A2-1                True  multivariate  172801   \n",
       "                WADI.A2-2                True  multivariate  172801   \n",
       "\n",
       "                              dimensions  contamination  num_anomalies  \\\n",
       "collection_name dataset_name                                             \n",
       "WADI            WADI.A2-1            127       0.057737             14   \n",
       "                WADI.A2-2            127       0.057737             14   \n",
       "\n",
       "                              min_anomaly_length  median_anomaly_length  \\\n",
       "collection_name dataset_name                                              \n",
       "WADI            WADI.A2-1                     88                    651   \n",
       "                WADI.A2-2                     88                    651   \n",
       "\n",
       "                              max_anomaly_length  mean  stddev        trend  \\\n",
       "collection_name dataset_name                                                  \n",
       "WADI            WADI.A2-1                   1741   NaN     NaN  kubic trend   \n",
       "                WADI.A2-2                   1741   NaN     NaN  kubic trend   \n",
       "\n",
       "                                stationarity  period_size  \n",
       "collection_name dataset_name                               \n",
       "WADI            WADI.A2-1     not_stationary          NaN  \n",
       "                WADI.A2-2     not_stationary          NaN  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dm.refresh()\n",
    "dm.df().loc[(slice(dataset_collection_name,dataset_collection_name), slice(None))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>timestamp</th>\n",
       "      <th>1_AIT_001_PV</th>\n",
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       "      <th>LEAK_DIFF_PRESSURE</th>\n",
       "      <th>PLANT_START_STOP_LOG</th>\n",
       "      <th>TOTAL_CONS_REQUIRED_FLOW</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>0</th>\n",
       "      <td>2017-10-09 18:00:00</td>\n",
       "      <td>164.210</td>\n",
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       "      <td>11.9972</td>\n",
       "      <td>482.480</td>\n",
       "      <td>0.331167</td>\n",
       "      <td>0.001273</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>48.4820</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>62.6226</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-10-09 18:00:01</td>\n",
       "      <td>164.210</td>\n",
       "      <td>0.529486</td>\n",
       "      <td>11.9972</td>\n",
       "      <td>482.480</td>\n",
       "      <td>0.331167</td>\n",
       "      <td>0.001273</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>48.4820</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>62.6226</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>0.529486</td>\n",
       "      <td>11.9972</td>\n",
       "      <td>482.480</td>\n",
       "      <td>0.331167</td>\n",
       "      <td>0.001273</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>48.4820</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>62.6226</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-10-09 18:00:03</td>\n",
       "      <td>164.210</td>\n",
       "      <td>0.529486</td>\n",
       "      <td>11.9972</td>\n",
       "      <td>482.480</td>\n",
       "      <td>0.331167</td>\n",
       "      <td>0.001273</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>48.4820</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>62.6226</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-10-09 18:00:04</td>\n",
       "      <td>164.210</td>\n",
       "      <td>0.529486</td>\n",
       "      <td>11.9972</td>\n",
       "      <td>482.480</td>\n",
       "      <td>0.331167</td>\n",
       "      <td>0.001273</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>48.4820</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>62.6226</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172796</th>\n",
       "      <td>2017-10-11 17:59:56</td>\n",
       "      <td>172.959</td>\n",
       "      <td>0.547483</td>\n",
       "      <td>11.9184</td>\n",
       "      <td>466.034</td>\n",
       "      <td>0.318217</td>\n",
       "      <td>0.001222</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>55.5587</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>59.3546</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172797</th>\n",
       "      <td>2017-10-11 17:59:57</td>\n",
       "      <td>172.959</td>\n",
       "      <td>0.547483</td>\n",
       "      <td>11.9184</td>\n",
       "      <td>466.034</td>\n",
       "      <td>0.318217</td>\n",
       "      <td>0.001222</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>55.5587</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>59.3546</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172798</th>\n",
       "      <td>2017-10-11 17:59:58</td>\n",
       "      <td>172.915</td>\n",
       "      <td>0.583479</td>\n",
       "      <td>11.9211</td>\n",
       "      <td>466.051</td>\n",
       "      <td>0.318317</td>\n",
       "      <td>0.001260</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>55.7260</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>58.8102</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172799</th>\n",
       "      <td>2017-10-11 17:59:59</td>\n",
       "      <td>172.915</td>\n",
       "      <td>0.583479</td>\n",
       "      <td>11.9211</td>\n",
       "      <td>466.051</td>\n",
       "      <td>0.318317</td>\n",
       "      <td>0.001260</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>55.7260</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>58.8102</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172800</th>\n",
       "      <td>2017-10-11 18:00:00</td>\n",
       "      <td>172.915</td>\n",
       "      <td>0.583479</td>\n",
       "      <td>11.9211</td>\n",
       "      <td>466.051</td>\n",
       "      <td>0.318317</td>\n",
       "      <td>0.001260</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>55.7260</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>58.8102</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>172801 rows × 129 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 timestamp  1_AIT_001_PV  1_AIT_002_PV  1_AIT_003_PV  \\\n",
       "Row                                                                    \n",
       "0      2017-10-09 18:00:00       164.210      0.529486       11.9972   \n",
       "1      2017-10-09 18:00:01       164.210      0.529486       11.9972   \n",
       "2      2017-10-09 18:00:02       164.210      0.529486       11.9972   \n",
       "3      2017-10-09 18:00:03       164.210      0.529486       11.9972   \n",
       "4      2017-10-09 18:00:04       164.210      0.529486       11.9972   \n",
       "...                    ...           ...           ...           ...   \n",
       "172796 2017-10-11 17:59:56       172.959      0.547483       11.9184   \n",
       "172797 2017-10-11 17:59:57       172.959      0.547483       11.9184   \n",
       "172798 2017-10-11 17:59:58       172.915      0.583479       11.9211   \n",
       "172799 2017-10-11 17:59:59       172.915      0.583479       11.9211   \n",
       "172800 2017-10-11 18:00:00       172.915      0.583479       11.9211   \n",
       "\n",
       "        1_AIT_004_PV  1_AIT_005_PV  1_FIT_001_PV  1_LS_001_AL  1_LS_002_AL  \\\n",
       "Row                                                                          \n",
       "0            482.480      0.331167      0.001273          0.0          0.0   \n",
       "1            482.480      0.331167      0.001273          0.0          0.0   \n",
       "2            482.480      0.331167      0.001273          0.0          0.0   \n",
       "3            482.480      0.331167      0.001273          0.0          0.0   \n",
       "4            482.480      0.331167      0.001273          0.0          0.0   \n",
       "...              ...           ...           ...          ...          ...   \n",
       "172796       466.034      0.318217      0.001222          0.0          0.0   \n",
       "172797       466.034      0.318217      0.001222          0.0          0.0   \n",
       "172798       466.051      0.318317      0.001260          0.0          0.0   \n",
       "172799       466.051      0.318317      0.001260          0.0          0.0   \n",
       "172800       466.051      0.318317      0.001260          0.0          0.0   \n",
       "\n",
       "        1_LT_001_PV  ...  3_MV_002_STATUS  3_MV_003_STATUS  3_P_001_STATUS  \\\n",
       "Row                  ...                                                     \n",
       "0           48.4820  ...              1.0              1.0             1.0   \n",
       "1           48.4820  ...              1.0              1.0             1.0   \n",
       "2           48.4820  ...              1.0              1.0             1.0   \n",
       "3           48.4820  ...              1.0              1.0             1.0   \n",
       "4           48.4820  ...              1.0              1.0             1.0   \n",
       "...             ...  ...              ...              ...             ...   \n",
       "172796      55.5587  ...              1.0              1.0             1.0   \n",
       "172797      55.5587  ...              1.0              1.0             1.0   \n",
       "172798      55.7260  ...              1.0              1.0             1.0   \n",
       "172799      55.7260  ...              1.0              1.0             1.0   \n",
       "172800      55.7260  ...              1.0              1.0             1.0   \n",
       "\n",
       "        3_P_002_STATUS  3_P_003_STATUS  3_P_004_STATUS  LEAK_DIFF_PRESSURE  \\\n",
       "Row                                                                          \n",
       "0                  1.0             1.0             1.0             62.6226   \n",
       "1                  1.0             1.0             1.0             62.6226   \n",
       "2                  1.0             1.0             1.0             62.6226   \n",
       "3                  1.0             1.0             1.0             62.6226   \n",
       "4                  1.0             1.0             1.0             62.6226   \n",
       "...                ...             ...             ...                 ...   \n",
       "172796             1.0             1.0             1.0             59.3546   \n",
       "172797             1.0             1.0             1.0             59.3546   \n",
       "172798             1.0             1.0             1.0             58.8102   \n",
       "172799             1.0             1.0             1.0             58.8102   \n",
       "172800             1.0             1.0             1.0             58.8102   \n",
       "\n",
       "        PLANT_START_STOP_LOG  TOTAL_CONS_REQUIRED_FLOW  is_anomaly  \n",
       "Row                                                                 \n",
       "0                        1.0                      0.39           0  \n",
       "1                        1.0                      0.39           0  \n",
       "2                        1.0                      0.39           0  \n",
       "3                        1.0                      0.39           0  \n",
       "4                        1.0                      0.39           0  \n",
       "...                      ...                       ...         ...  \n",
       "172796                   1.0                      0.00           0  \n",
       "172797                   1.0                      0.00           0  \n",
       "172798                   1.0                      0.00           0  \n",
       "172799                   1.0                      0.00           0  \n",
       "172800                   1.0                      0.00           0  \n",
       "\n",
       "[172801 rows x 129 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(source_folder / \"WADI_attackdataLABLE.csv\",\n",
    "                 skiprows=1,\n",
    "                 skip_blank_lines=True,\n",
    "                 index_col=0).iloc[:-2, :]\n",
    "# fix index\n",
    "df.index = df.index.astype(np.int_) - 1\n",
    "df.index.name = df.index.name.strip()\n",
    "\n",
    "# fix column names\n",
    "df.columns = [c.strip() for c in df.columns]\n",
    "\n",
    "# fix timestamps\n",
    "s_hour = (18 + (df.index // 3600)) % 24\n",
    "s_timestamp = df[\"Date\"] + s_hour.map(\" {:02.0f}:\".format) + df[\"Time\"]\n",
    "s_timestamp = pd.to_datetime(s_timestamp, infer_datetime_format=True)\n",
    "df = df.drop(columns=[\"Date\", \"Time\"])\n",
    "df.insert(0, \"timestamp\", s_timestamp)\n",
    "\n",
    "# align anomaly label\n",
    "s_label = df[\"Attack LABLE (1:No Attack, -1:Attack)\"]\n",
    "df = df.drop(columns=[\"Attack LABLE (1:No Attack, -1:Attack)\"])\n",
    "df[\"is_anomaly\"] = 0\n",
    "df.loc[s_label == -1, \"is_anomaly\"] = 1\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_tmp = df[[\"1_AIT_001_PV\", \"1_FIT_001_PV\", \"1_LT_001_PV\", \"LEAK_DIFF_PRESSURE\", \"is_anomaly\"]]\n",
    "df_tmp.iloc[:, :-1].plot()\n",
    "s = df_tmp[\"is_anomaly\"].diff()\n",
    "for begin, end in zip(s[s == -1].index, s[s == 1].index):\n",
    "    plt.gca().add_patch(matplotlib.patches.Rectangle((begin, 0), end - begin, df_tmp.max().max(), color=\"red\", alpha=0.25))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>1_AIT_001_PV</th>\n",
       "      <th>1_AIT_002_PV</th>\n",
       "      <th>1_AIT_003_PV</th>\n",
       "      <th>1_AIT_004_PV</th>\n",
       "      <th>1_AIT_005_PV</th>\n",
       "      <th>1_FIT_001_PV</th>\n",
       "      <th>1_LS_001_AL</th>\n",
       "      <th>1_LS_002_AL</th>\n",
       "      <th>1_LT_001_PV</th>\n",
       "      <th>...</th>\n",
       "      <th>3_MV_002_STATUS</th>\n",
       "      <th>3_MV_003_STATUS</th>\n",
       "      <th>3_P_001_STATUS</th>\n",
       "      <th>3_P_002_STATUS</th>\n",
       "      <th>3_P_003_STATUS</th>\n",
       "      <th>3_P_004_STATUS</th>\n",
       "      <th>LEAK_DIFF_PRESSURE</th>\n",
       "      <th>PLANT_START_STOP_LOG</th>\n",
       "      <th>TOTAL_CONS_REQUIRED_FLOW</th>\n",
       "      <th>is_anomaly</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Row</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-09-25 18:00:00</td>\n",
       "      <td>171.155</td>\n",
       "      <td>0.619473</td>\n",
       "      <td>11.5759</td>\n",
       "      <td>504.645</td>\n",
       "      <td>0.318319</td>\n",
       "      <td>0.001157</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>47.8911</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>67.9651</td>\n",
       "      <td>1</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-09-25 18:00:01</td>\n",
       "      <td>171.155</td>\n",
       "      <td>0.619473</td>\n",
       "      <td>11.5759</td>\n",
       "      <td>504.645</td>\n",
       "      <td>0.318319</td>\n",
       "      <td>0.001157</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>47.8911</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>67.9651</td>\n",
       "      <td>1</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-09-25 18:00:02</td>\n",
       "      <td>171.155</td>\n",
       "      <td>0.619473</td>\n",
       "      <td>11.5759</td>\n",
       "      <td>504.645</td>\n",
       "      <td>0.318319</td>\n",
       "      <td>0.001157</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>47.8911</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>67.9651</td>\n",
       "      <td>1</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-09-25 18:00:03</td>\n",
       "      <td>171.155</td>\n",
       "      <td>0.607477</td>\n",
       "      <td>11.5725</td>\n",
       "      <td>504.673</td>\n",
       "      <td>0.318438</td>\n",
       "      <td>0.001207</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>47.7503</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>67.1948</td>\n",
       "      <td>1</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-09-25 18:00:04</td>\n",
       "      <td>171.155</td>\n",
       "      <td>0.607477</td>\n",
       "      <td>11.5725</td>\n",
       "      <td>504.673</td>\n",
       "      <td>0.318438</td>\n",
       "      <td>0.001207</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>47.7503</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>67.1948</td>\n",
       "      <td>1</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1048566</th>\n",
       "      <td>2017-10-07 21:16:06</td>\n",
       "      <td>175.855</td>\n",
       "      <td>0.589478</td>\n",
       "      <td>11.8941</td>\n",
       "      <td>479.191</td>\n",
       "      <td>0.331571</td>\n",
       "      <td>0.001128</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>48.1129</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>60.6305</td>\n",
       "      <td>1</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1048567</th>\n",
       "      <td>2017-10-07 21:16:07</td>\n",
       "      <td>175.855</td>\n",
       "      <td>0.589478</td>\n",
       "      <td>11.8941</td>\n",
       "      <td>479.191</td>\n",
       "      <td>0.331571</td>\n",
       "      <td>0.001128</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>48.1129</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>60.6305</td>\n",
       "      <td>1</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1048568</th>\n",
       "      <td>2017-10-07 21:16:08</td>\n",
       "      <td>175.855</td>\n",
       "      <td>0.589478</td>\n",
       "      <td>11.8941</td>\n",
       "      <td>479.191</td>\n",
       "      <td>0.331571</td>\n",
       "      <td>0.001128</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>48.1129</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>60.6305</td>\n",
       "      <td>1</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1048569</th>\n",
       "      <td>2017-10-07 21:16:09</td>\n",
       "      <td>175.896</td>\n",
       "      <td>0.613476</td>\n",
       "      <td>11.8913</td>\n",
       "      <td>479.224</td>\n",
       "      <td>0.331622</td>\n",
       "      <td>0.001173</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>48.0348</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>60.4477</td>\n",
       "      <td>1</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1048570</th>\n",
       "      <td>2017-10-07 21:16:10</td>\n",
       "      <td>175.896</td>\n",
       "      <td>0.613476</td>\n",
       "      <td>11.8913</td>\n",
       "      <td>479.224</td>\n",
       "      <td>0.331622</td>\n",
       "      <td>0.001173</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>48.0348</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>60.4477</td>\n",
       "      <td>1</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>784571 rows × 129 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  timestamp  1_AIT_001_PV  1_AIT_002_PV  1_AIT_003_PV  \\\n",
       "Row                                                                     \n",
       "0       2017-09-25 18:00:00       171.155      0.619473       11.5759   \n",
       "1       2017-09-25 18:00:01       171.155      0.619473       11.5759   \n",
       "2       2017-09-25 18:00:02       171.155      0.619473       11.5759   \n",
       "3       2017-09-25 18:00:03       171.155      0.607477       11.5725   \n",
       "4       2017-09-25 18:00:04       171.155      0.607477       11.5725   \n",
       "...                     ...           ...           ...           ...   \n",
       "1048566 2017-10-07 21:16:06       175.855      0.589478       11.8941   \n",
       "1048567 2017-10-07 21:16:07       175.855      0.589478       11.8941   \n",
       "1048568 2017-10-07 21:16:08       175.855      0.589478       11.8941   \n",
       "1048569 2017-10-07 21:16:09       175.896      0.613476       11.8913   \n",
       "1048570 2017-10-07 21:16:10       175.896      0.613476       11.8913   \n",
       "\n",
       "         1_AIT_004_PV  1_AIT_005_PV  1_FIT_001_PV  1_LS_001_AL  1_LS_002_AL  \\\n",
       "Row                                                                           \n",
       "0             504.645      0.318319      0.001157            0            0   \n",
       "1             504.645      0.318319      0.001157            0            0   \n",
       "2             504.645      0.318319      0.001157            0            0   \n",
       "3             504.673      0.318438      0.001207            0            0   \n",
       "4             504.673      0.318438      0.001207            0            0   \n",
       "...               ...           ...           ...          ...          ...   \n",
       "1048566       479.191      0.331571      0.001128            0            0   \n",
       "1048567       479.191      0.331571      0.001128            0            0   \n",
       "1048568       479.191      0.331571      0.001128            0            0   \n",
       "1048569       479.224      0.331622      0.001173            0            0   \n",
       "1048570       479.224      0.331622      0.001173            0            0   \n",
       "\n",
       "         1_LT_001_PV  ...  3_MV_002_STATUS  3_MV_003_STATUS  3_P_001_STATUS  \\\n",
       "Row                   ...                                                     \n",
       "0            47.8911  ...                1                1               1   \n",
       "1            47.8911  ...                1                1               1   \n",
       "2            47.8911  ...                1                1               1   \n",
       "3            47.7503  ...                1                1               1   \n",
       "4            47.7503  ...                1                1               1   \n",
       "...              ...  ...              ...              ...             ...   \n",
       "1048566      48.1129  ...                1                1               1   \n",
       "1048567      48.1129  ...                1                1               1   \n",
       "1048568      48.1129  ...                1                1               1   \n",
       "1048569      48.0348  ...                1                1               1   \n",
       "1048570      48.0348  ...                1                1               1   \n",
       "\n",
       "         3_P_002_STATUS  3_P_003_STATUS  3_P_004_STATUS  LEAK_DIFF_PRESSURE  \\\n",
       "Row                                                                           \n",
       "0                     1               1               1             67.9651   \n",
       "1                     1               1               1             67.9651   \n",
       "2                     1               1               1             67.9651   \n",
       "3                     1               1               1             67.1948   \n",
       "4                     1               1               1             67.1948   \n",
       "...                 ...             ...             ...                 ...   \n",
       "1048566               1               1               1             60.6305   \n",
       "1048567               1               1               1             60.6305   \n",
       "1048568               1               1               1             60.6305   \n",
       "1048569               1               1               1             60.4477   \n",
       "1048570               1               1               1             60.4477   \n",
       "\n",
       "         PLANT_START_STOP_LOG  TOTAL_CONS_REQUIRED_FLOW  is_anomaly  \n",
       "Row                                                                  \n",
       "0                           1                      0.68           0  \n",
       "1                           1                      0.68           0  \n",
       "2                           1                      0.68           0  \n",
       "3                           1                      0.68           0  \n",
       "4                           1                      0.68           0  \n",
       "...                       ...                       ...         ...  \n",
       "1048566                     1                      0.25           0  \n",
       "1048567                     1                      0.25           0  \n",
       "1048568                     1                      0.25           0  \n",
       "1048569                     1                      0.25           0  \n",
       "1048570                     1                      0.25           0  \n",
       "\n",
       "[784571 rows x 129 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.read_csv(source_folder / \"WADI_14days_new.csv\",\n",
    "                 skip_blank_lines=True,\n",
    "                 index_col=0)\n",
    "# fix index\n",
    "df2.index = df2.index.astype(np.int_) - 1\n",
    "df2.index.name = df2.index.name.strip()\n",
    "\n",
    "# fix column names\n",
    "df2.columns = [c.strip() for c in df2.columns]\n",
    "\n",
    "# fix timestamps\n",
    "s_hour = (18 + (df2.index // 3600)) % 24\n",
    "s_timestamp = df2[\"Date\"] + s_hour.map(\" {:02.0f}:\".format) + df2[\"Time\"]\n",
    "s_timestamp = pd.to_datetime(s_timestamp, infer_datetime_format=True)\n",
    "df2 = df2.drop(columns=[\"Date\", \"Time\"])\n",
    "df2.insert(0, \"timestamp\", s_timestamp)\n",
    "\n",
    "# insert anomaly label\n",
    "df2[\"is_anomaly\"] = 0\n",
    "\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_tmp = df2.set_index(\"timestamp\")\n",
    "df_tmp = df_tmp[[\"1_AIT_001_PV\", \"1_FIT_001_PV\", \"1_LT_001_PV\", \"LEAK_DIFF_PRESSURE\"]]#.iloc[330000:340000]\n",
    "df_tmp.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems that the authors just removed the parts of the time series, where there were instabilities in the system.\n",
    "We decide to cut the time series in two parts (the instabilities were roughly in the middle of the experiment) and use each as a separate training dataset.\n",
    "\n",
    "Cut point after 2017-09-29 15:19:58, because of the jump from row index 335998 to 599999:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Row\n",
       "599999   3 days 01:20:01\n",
       "Name: timestamp, dtype: timedelta64[ns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = df2[\"timestamp\"].diff()\n",
    "s = s[s > pd.Timedelta(1, unit=\"s\")]\n",
    "cut_index = s.index[0]\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>1_AIT_001_PV</th>\n",
       "      <th>1_AIT_002_PV</th>\n",
       "      <th>1_AIT_003_PV</th>\n",
       "      <th>1_AIT_004_PV</th>\n",
       "      <th>1_AIT_005_PV</th>\n",
       "      <th>1_FIT_001_PV</th>\n",
       "      <th>1_LS_001_AL</th>\n",
       "      <th>1_LS_002_AL</th>\n",
       "      <th>1_LT_001_PV</th>\n",
       "      <th>...</th>\n",
       "      <th>3_MV_002_STATUS</th>\n",
       "      <th>3_MV_003_STATUS</th>\n",
       "      <th>3_P_001_STATUS</th>\n",
       "      <th>3_P_002_STATUS</th>\n",
       "      <th>3_P_003_STATUS</th>\n",
       "      <th>3_P_004_STATUS</th>\n",
       "      <th>LEAK_DIFF_PRESSURE</th>\n",
       "      <th>PLANT_START_STOP_LOG</th>\n",
       "      <th>TOTAL_CONS_REQUIRED_FLOW</th>\n",
       "      <th>is_anomaly</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Row</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>335995</th>\n",
       "      <td>2017-09-29 15:19:55</td>\n",
       "      <td>175.080</td>\n",
       "      <td>0.565481</td>\n",
       "      <td>11.6779</td>\n",
       "      <td>510.257</td>\n",
       "      <td>0.338041</td>\n",
       "      <td>0.001127</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>57.8136</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>59.5862</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335996</th>\n",
       "      <td>2017-09-29 15:19:56</td>\n",
       "      <td>175.080</td>\n",
       "      <td>0.565481</td>\n",
       "      <td>11.6779</td>\n",
       "      <td>510.257</td>\n",
       "      <td>0.338041</td>\n",
       "      <td>0.001127</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>57.8136</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>59.5862</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335997</th>\n",
       "      <td>2017-09-29 15:19:57</td>\n",
       "      <td>175.025</td>\n",
       "      <td>0.553482</td>\n",
       "      <td>11.6739</td>\n",
       "      <td>510.257</td>\n",
       "      <td>0.338080</td>\n",
       "      <td>0.001244</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>57.7764</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>59.6988</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335998</th>\n",
       "      <td>2017-09-29 15:19:58</td>\n",
       "      <td>175.025</td>\n",
       "      <td>0.553482</td>\n",
       "      <td>11.6739</td>\n",
       "      <td>510.257</td>\n",
       "      <td>0.338080</td>\n",
       "      <td>0.001244</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>57.7764</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>59.6988</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>599999</th>\n",
       "      <td>2017-10-02 16:39:59</td>\n",
       "      <td>156.378</td>\n",
       "      <td>0.691463</td>\n",
       "      <td>11.8310</td>\n",
       "      <td>480.409</td>\n",
       "      <td>0.260597</td>\n",
       "      <td>1.898500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>47.4149</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>63.2665</td>\n",
       "      <td>1</td>\n",
       "      <td>1.14</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600000</th>\n",
       "      <td>2017-10-02 16:40:00</td>\n",
       "      <td>156.378</td>\n",
       "      <td>0.691463</td>\n",
       "      <td>11.8310</td>\n",
       "      <td>480.409</td>\n",
       "      <td>0.260597</td>\n",
       "      <td>1.898500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>47.4149</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>63.2665</td>\n",
       "      <td>1</td>\n",
       "      <td>1.14</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600001</th>\n",
       "      <td>2017-10-02 16:40:01</td>\n",
       "      <td>156.378</td>\n",
       "      <td>0.691463</td>\n",
       "      <td>11.8310</td>\n",
       "      <td>480.409</td>\n",
       "      <td>0.260597</td>\n",
       "      <td>1.898500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>47.4149</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>63.2665</td>\n",
       "      <td>1</td>\n",
       "      <td>1.14</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600002</th>\n",
       "      <td>2017-10-02 16:40:02</td>\n",
       "      <td>156.378</td>\n",
       "      <td>0.691463</td>\n",
       "      <td>11.8310</td>\n",
       "      <td>480.409</td>\n",
       "      <td>0.260597</td>\n",
       "      <td>1.898500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>47.4149</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>63.2665</td>\n",
       "      <td>1</td>\n",
       "      <td>1.14</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 129 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 timestamp  1_AIT_001_PV  1_AIT_002_PV  1_AIT_003_PV  \\\n",
       "Row                                                                    \n",
       "335995 2017-09-29 15:19:55       175.080      0.565481       11.6779   \n",
       "335996 2017-09-29 15:19:56       175.080      0.565481       11.6779   \n",
       "335997 2017-09-29 15:19:57       175.025      0.553482       11.6739   \n",
       "335998 2017-09-29 15:19:58       175.025      0.553482       11.6739   \n",
       "599999 2017-10-02 16:39:59       156.378      0.691463       11.8310   \n",
       "600000 2017-10-02 16:40:00       156.378      0.691463       11.8310   \n",
       "600001 2017-10-02 16:40:01       156.378      0.691463       11.8310   \n",
       "600002 2017-10-02 16:40:02       156.378      0.691463       11.8310   \n",
       "\n",
       "        1_AIT_004_PV  1_AIT_005_PV  1_FIT_001_PV  1_LS_001_AL  1_LS_002_AL  \\\n",
       "Row                                                                          \n",
       "335995       510.257      0.338041      0.001127            0            0   \n",
       "335996       510.257      0.338041      0.001127            0            0   \n",
       "335997       510.257      0.338080      0.001244            0            0   \n",
       "335998       510.257      0.338080      0.001244            0            0   \n",
       "599999       480.409      0.260597      1.898500            0            0   \n",
       "600000       480.409      0.260597      1.898500            0            0   \n",
       "600001       480.409      0.260597      1.898500            0            0   \n",
       "600002       480.409      0.260597      1.898500            0            0   \n",
       "\n",
       "        1_LT_001_PV  ...  3_MV_002_STATUS  3_MV_003_STATUS  3_P_001_STATUS  \\\n",
       "Row                  ...                                                     \n",
       "335995      57.8136  ...                1                1               1   \n",
       "335996      57.8136  ...                1                1               1   \n",
       "335997      57.7764  ...                1                1               1   \n",
       "335998      57.7764  ...                1                1               1   \n",
       "599999      47.4149  ...                1                1               1   \n",
       "600000      47.4149  ...                1                1               1   \n",
       "600001      47.4149  ...                1                1               1   \n",
       "600002      47.4149  ...                1                1               1   \n",
       "\n",
       "        3_P_002_STATUS  3_P_003_STATUS  3_P_004_STATUS  LEAK_DIFF_PRESSURE  \\\n",
       "Row                                                                          \n",
       "335995               1               1               1             59.5862   \n",
       "335996               1               1               1             59.5862   \n",
       "335997               1               1               1             59.6988   \n",
       "335998               1               1               1             59.6988   \n",
       "599999               1               1               1             63.2665   \n",
       "600000               1               1               1             63.2665   \n",
       "600001               1               1               1             63.2665   \n",
       "600002               1               1               1             63.2665   \n",
       "\n",
       "        PLANT_START_STOP_LOG  TOTAL_CONS_REQUIRED_FLOW  is_anomaly  \n",
       "Row                                                                 \n",
       "335995                     1                      0.00           0  \n",
       "335996                     1                      0.00           0  \n",
       "335997                     1                      0.00           0  \n",
       "335998                     1                      0.00           0  \n",
       "599999                     1                      1.14           0  \n",
       "600000                     1                      1.14           0  \n",
       "600001                     1                      1.14           0  \n",
       "600002                     1                      1.14           0  \n",
       "\n",
       "[8 rows x 129 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.loc[335995:600002]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>1_AIT_001_PV</th>\n",
       "      <th>1_AIT_002_PV</th>\n",
       "      <th>1_AIT_003_PV</th>\n",
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       "      <th>0</th>\n",
       "      <td>2017-09-25 18:00:00</td>\n",
       "      <td>171.155</td>\n",
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       "      <td>504.645</td>\n",
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       "      <td>0.619473</td>\n",
       "      <td>11.5759</td>\n",
       "      <td>504.645</td>\n",
       "      <td>0.318319</td>\n",
       "      <td>0.001157</td>\n",
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       "      <td>1</td>\n",
       "      <td>67.9651</td>\n",
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       "      <td>0.68</td>\n",
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       "      <th>2</th>\n",
       "      <td>2017-09-25 18:00:02</td>\n",
       "      <td>171.155</td>\n",
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       "      <td>11.5759</td>\n",
       "      <td>504.645</td>\n",
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       "      <th>3</th>\n",
       "      <td>2017-09-25 18:00:03</td>\n",
       "      <td>171.155</td>\n",
       "      <td>0.607477</td>\n",
       "      <td>11.5725</td>\n",
       "      <td>504.673</td>\n",
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       "      <td>0.001207</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>67.1948</td>\n",
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       "      <td>2017-09-25 18:00:04</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335994</th>\n",
       "      <td>2017-09-29 15:19:54</td>\n",
       "      <td>175.080</td>\n",
       "      <td>0.565481</td>\n",
       "      <td>11.6779</td>\n",
       "      <td>510.257</td>\n",
       "      <td>0.338041</td>\n",
       "      <td>0.001127</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>57.8136</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>59.5862</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335995</th>\n",
       "      <td>2017-09-29 15:19:55</td>\n",
       "      <td>175.080</td>\n",
       "      <td>0.565481</td>\n",
       "      <td>11.6779</td>\n",
       "      <td>510.257</td>\n",
       "      <td>0.338041</td>\n",
       "      <td>0.001127</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>57.8136</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>59.5862</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335996</th>\n",
       "      <td>2017-09-29 15:19:56</td>\n",
       "      <td>175.080</td>\n",
       "      <td>0.565481</td>\n",
       "      <td>11.6779</td>\n",
       "      <td>510.257</td>\n",
       "      <td>0.338041</td>\n",
       "      <td>0.001127</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>57.8136</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>59.5862</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335997</th>\n",
       "      <td>2017-09-29 15:19:57</td>\n",
       "      <td>175.025</td>\n",
       "      <td>0.553482</td>\n",
       "      <td>11.6739</td>\n",
       "      <td>510.257</td>\n",
       "      <td>0.338080</td>\n",
       "      <td>0.001244</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>57.7764</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>59.6988</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335998</th>\n",
       "      <td>2017-09-29 15:19:58</td>\n",
       "      <td>175.025</td>\n",
       "      <td>0.553482</td>\n",
       "      <td>11.6739</td>\n",
       "      <td>510.257</td>\n",
       "      <td>0.338080</td>\n",
       "      <td>0.001244</td>\n",
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       "      <td>0</td>\n",
       "      <td>57.7764</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>59.6988</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>335999 rows × 129 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 timestamp  1_AIT_001_PV  1_AIT_002_PV  1_AIT_003_PV  \\\n",
       "Row                                                                    \n",
       "0      2017-09-25 18:00:00       171.155      0.619473       11.5759   \n",
       "1      2017-09-25 18:00:01       171.155      0.619473       11.5759   \n",
       "2      2017-09-25 18:00:02       171.155      0.619473       11.5759   \n",
       "3      2017-09-25 18:00:03       171.155      0.607477       11.5725   \n",
       "4      2017-09-25 18:00:04       171.155      0.607477       11.5725   \n",
       "...                    ...           ...           ...           ...   \n",
       "335994 2017-09-29 15:19:54       175.080      0.565481       11.6779   \n",
       "335995 2017-09-29 15:19:55       175.080      0.565481       11.6779   \n",
       "335996 2017-09-29 15:19:56       175.080      0.565481       11.6779   \n",
       "335997 2017-09-29 15:19:57       175.025      0.553482       11.6739   \n",
       "335998 2017-09-29 15:19:58       175.025      0.553482       11.6739   \n",
       "\n",
       "        1_AIT_004_PV  1_AIT_005_PV  1_FIT_001_PV  1_LS_001_AL  1_LS_002_AL  \\\n",
       "Row                                                                          \n",
       "0            504.645      0.318319      0.001157            0            0   \n",
       "1            504.645      0.318319      0.001157            0            0   \n",
       "2            504.645      0.318319      0.001157            0            0   \n",
       "3            504.673      0.318438      0.001207            0            0   \n",
       "4            504.673      0.318438      0.001207            0            0   \n",
       "...              ...           ...           ...          ...          ...   \n",
       "335994       510.257      0.338041      0.001127            0            0   \n",
       "335995       510.257      0.338041      0.001127            0            0   \n",
       "335996       510.257      0.338041      0.001127            0            0   \n",
       "335997       510.257      0.338080      0.001244            0            0   \n",
       "335998       510.257      0.338080      0.001244            0            0   \n",
       "\n",
       "        1_LT_001_PV  ...  3_MV_002_STATUS  3_MV_003_STATUS  3_P_001_STATUS  \\\n",
       "Row                  ...                                                     \n",
       "0           47.8911  ...                1                1               1   \n",
       "1           47.8911  ...                1                1               1   \n",
       "2           47.8911  ...                1                1               1   \n",
       "3           47.7503  ...                1                1               1   \n",
       "4           47.7503  ...                1                1               1   \n",
       "...             ...  ...              ...              ...             ...   \n",
       "335994      57.8136  ...                1                1               1   \n",
       "335995      57.8136  ...                1                1               1   \n",
       "335996      57.8136  ...                1                1               1   \n",
       "335997      57.7764  ...                1                1               1   \n",
       "335998      57.7764  ...                1                1               1   \n",
       "\n",
       "        3_P_002_STATUS  3_P_003_STATUS  3_P_004_STATUS  LEAK_DIFF_PRESSURE  \\\n",
       "Row                                                                          \n",
       "0                    1               1               1             67.9651   \n",
       "1                    1               1               1             67.9651   \n",
       "2                    1               1               1             67.9651   \n",
       "3                    1               1               1             67.1948   \n",
       "4                    1               1               1             67.1948   \n",
       "...                ...             ...             ...                 ...   \n",
       "335994               1               1               1             59.5862   \n",
       "335995               1               1               1             59.5862   \n",
       "335996               1               1               1             59.5862   \n",
       "335997               1               1               1             59.6988   \n",
       "335998               1               1               1             59.6988   \n",
       "\n",
       "        PLANT_START_STOP_LOG  TOTAL_CONS_REQUIRED_FLOW  is_anomaly  \n",
       "Row                                                                 \n",
       "0                          1                      0.68           0  \n",
       "1                          1                      0.68           0  \n",
       "2                          1                      0.68           0  \n",
       "3                          1                      0.68           0  \n",
       "4                          1                      0.68           0  \n",
       "...                      ...                       ...         ...  \n",
       "335994                     1                      0.00           0  \n",
       "335995                     1                      0.00           0  \n",
       "335996                     1                      0.00           0  \n",
       "335997                     1                      0.00           0  \n",
       "335998                     1                      0.00           0  \n",
       "\n",
       "[335999 rows x 129 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.loc[:cut_index-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>1_AIT_001_PV</th>\n",
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       "      <th>599999</th>\n",
       "      <td>2017-10-02 16:39:59</td>\n",
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       "      <th>600000</th>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>63.2665</td>\n",
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       "      <th>600001</th>\n",
       "      <td>2017-10-02 16:40:01</td>\n",
       "      <td>156.378</td>\n",
       "      <td>0.691463</td>\n",
       "      <td>11.8310</td>\n",
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       "      <th>600002</th>\n",
       "      <td>2017-10-02 16:40:02</td>\n",
       "      <td>156.378</td>\n",
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       "    <tr>\n",
       "      <th>600003</th>\n",
       "      <td>2017-10-02 16:40:03</td>\n",
       "      <td>156.378</td>\n",
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       "      <td>1</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1048566</th>\n",
       "      <td>2017-10-07 21:16:06</td>\n",
       "      <td>175.855</td>\n",
       "      <td>0.589478</td>\n",
       "      <td>11.8941</td>\n",
       "      <td>479.191</td>\n",
       "      <td>0.331571</td>\n",
       "      <td>0.001128</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>48.1129</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>60.6305</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1048567</th>\n",
       "      <td>2017-10-07 21:16:07</td>\n",
       "      <td>175.855</td>\n",
       "      <td>0.589478</td>\n",
       "      <td>11.8941</td>\n",
       "      <td>479.191</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>60.6305</td>\n",
       "      <td>1</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1048568</th>\n",
       "      <td>2017-10-07 21:16:08</td>\n",
       "      <td>175.855</td>\n",
       "      <td>0.589478</td>\n",
       "      <td>11.8941</td>\n",
       "      <td>479.191</td>\n",
       "      <td>0.331571</td>\n",
       "      <td>0.001128</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>48.1129</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>60.6305</td>\n",
       "      <td>1</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1048569</th>\n",
       "      <td>2017-10-07 21:16:09</td>\n",
       "      <td>175.896</td>\n",
       "      <td>0.613476</td>\n",
       "      <td>11.8913</td>\n",
       "      <td>479.224</td>\n",
       "      <td>0.331622</td>\n",
       "      <td>0.001173</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>48.0348</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>60.4477</td>\n",
       "      <td>1</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1048570</th>\n",
       "      <td>2017-10-07 21:16:10</td>\n",
       "      <td>175.896</td>\n",
       "      <td>0.613476</td>\n",
       "      <td>11.8913</td>\n",
       "      <td>479.224</td>\n",
       "      <td>0.331622</td>\n",
       "      <td>0.001173</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>48.0348</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>60.4477</td>\n",
       "      <td>1</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>448572 rows × 129 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  timestamp  1_AIT_001_PV  1_AIT_002_PV  1_AIT_003_PV  \\\n",
       "Row                                                                     \n",
       "599999  2017-10-02 16:39:59       156.378      0.691463       11.8310   \n",
       "600000  2017-10-02 16:40:00       156.378      0.691463       11.8310   \n",
       "600001  2017-10-02 16:40:01       156.378      0.691463       11.8310   \n",
       "600002  2017-10-02 16:40:02       156.378      0.691463       11.8310   \n",
       "600003  2017-10-02 16:40:03       156.378      0.691463       11.8310   \n",
       "...                     ...           ...           ...           ...   \n",
       "1048566 2017-10-07 21:16:06       175.855      0.589478       11.8941   \n",
       "1048567 2017-10-07 21:16:07       175.855      0.589478       11.8941   \n",
       "1048568 2017-10-07 21:16:08       175.855      0.589478       11.8941   \n",
       "1048569 2017-10-07 21:16:09       175.896      0.613476       11.8913   \n",
       "1048570 2017-10-07 21:16:10       175.896      0.613476       11.8913   \n",
       "\n",
       "         1_AIT_004_PV  1_AIT_005_PV  1_FIT_001_PV  1_LS_001_AL  1_LS_002_AL  \\\n",
       "Row                                                                           \n",
       "599999        480.409      0.260597      1.898500            0            0   \n",
       "600000        480.409      0.260597      1.898500            0            0   \n",
       "600001        480.409      0.260597      1.898500            0            0   \n",
       "600002        480.409      0.260597      1.898500            0            0   \n",
       "600003        480.409      0.260597      1.898500            0            0   \n",
       "...               ...           ...           ...          ...          ...   \n",
       "1048566       479.191      0.331571      0.001128            0            0   \n",
       "1048567       479.191      0.331571      0.001128            0            0   \n",
       "1048568       479.191      0.331571      0.001128            0            0   \n",
       "1048569       479.224      0.331622      0.001173            0            0   \n",
       "1048570       479.224      0.331622      0.001173            0            0   \n",
       "\n",
       "         1_LT_001_PV  ...  3_MV_002_STATUS  3_MV_003_STATUS  3_P_001_STATUS  \\\n",
       "Row                   ...                                                     \n",
       "599999       47.4149  ...                1                1               1   \n",
       "600000       47.4149  ...                1                1               1   \n",
       "600001       47.4149  ...                1                1               1   \n",
       "600002       47.4149  ...                1                1               1   \n",
       "600003       47.4149  ...                1                1               1   \n",
       "...              ...  ...              ...              ...             ...   \n",
       "1048566      48.1129  ...                1                1               1   \n",
       "1048567      48.1129  ...                1                1               1   \n",
       "1048568      48.1129  ...                1                1               1   \n",
       "1048569      48.0348  ...                1                1               1   \n",
       "1048570      48.0348  ...                1                1               1   \n",
       "\n",
       "         3_P_002_STATUS  3_P_003_STATUS  3_P_004_STATUS  LEAK_DIFF_PRESSURE  \\\n",
       "Row                                                                           \n",
       "599999                1               1               1             63.2665   \n",
       "600000                1               1               1             63.2665   \n",
       "600001                1               1               1             63.2665   \n",
       "600002                1               1               1             63.2665   \n",
       "600003                1               1               1             63.2665   \n",
       "...                 ...             ...             ...                 ...   \n",
       "1048566               1               1               1             60.6305   \n",
       "1048567               1               1               1             60.6305   \n",
       "1048568               1               1               1             60.6305   \n",
       "1048569               1               1               1             60.4477   \n",
       "1048570               1               1               1             60.4477   \n",
       "\n",
       "         PLANT_START_STOP_LOG  TOTAL_CONS_REQUIRED_FLOW  is_anomaly  \n",
       "Row                                                                  \n",
       "599999                      1                      1.14           0  \n",
       "600000                      1                      1.14           0  \n",
       "600001                      1                      1.14           0  \n",
       "600002                      1                      1.14           0  \n",
       "600003                      1                      1.14           0  \n",
       "...                       ...                       ...         ...  \n",
       "1048566                     1                      0.25           0  \n",
       "1048567                     1                      0.25           0  \n",
       "1048568                     1                      0.25           0  \n",
       "1048569                     1                      0.25           0  \n",
       "1048570                     1                      0.25           0  \n",
       "\n",
       "[448572 rows x 129 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.loc[cut_index:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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